Download Forward Prediction in the Posterior Parietal Cortex and Dynamic

PERSPECTIVE
published: 26 October 2016
doi: 10.3389/fnint.2016.00035
Forward Prediction in the Posterior
Parietal Cortex and Dynamic
Brain-Machine Interface
He Cui 1,2 *
1
Institute of Neuroscience, Chinese Academy of Sciences (CAS), Shanghai, China, 2 Key Laboratory of Primate
Neurobiology, Chinese Academy of Sciences (CAS), Shanghai, China
While remarkable progress has been made in brain-machine interfaces (BMIs) over the
past two decades, it is still difficult to utilize neural signals to drive artificial actuators to
produce predictive movements in response to dynamic stimuli. In contrast to naturalistic
limb movements largely based on forward planning, brain-controlled neuroprosthetics
mainly rely on feedback without prior trajectory formation. As an important sensorimotor
interface integrating multisensory inputs and efference copy, the posterior parietal cortex
(PPC) might play a proactive role in predictive motor control. Here it is proposed that
predictive neural activity in PPC could be decoded to provide prosthetic control signals
for guiding BMI systems in dynamic environments.
Keywords: neuroprosthetics, decoding, neuroengineering, internal model, motor control, paralysis
INTRODUCTION
Edited by:
Henry H. Yin,
Duke University, USA
Reviewed by:
Hugo Merchant,
National Autonomous University of
Mexico, Mexico
Xin Jin,
Salk Institute for Biological
Studies, USA
*Correspondence:
He Cui
[email protected]
Received: 17 August 2016
Accepted: 10 October 2016
Published: 26 October 2016
Citation:
Cui H (2016) Forward Prediction in
the Posterior Parietal Cortex and
Dynamic Brain-Machine Interface.
Front. Integr. Neurosci. 10:35.
doi: 10.3389/fnint.2016.00035
To interact with a changing world, such as in tracking and intercepting moving objects, the brain
must overcome pervasive sensorimotor delays (Nijhawan, 2008; Franklin and Wolpert, 2011).
Although it has been proposed that compensating for these inherent delays is based on bottom-up
sensory extrapolation (e.g., the flash-lag effect, Nijhawan, 1994; Nijhawan and Wu, 2009), the
prevalent view of sensorimotor control posits that action planning relies on forward models based
on an intimate interplay between sensory inflow and motor outflow, rather than a hierarchical
transformation from extrinsic stimuli to intrinsic muscular activity (Wolpert et al., 1995; Shadmehr
and Mussa-Ivaldi, 2012).
FORWARD MODEL FOR PREDICTIVE SENSORIMOTOR
CONTROL
Movement planning predominately arises from an internal prediction of future states of body
and environment, instead of merely relying on sensory responses. Since the emergence of
the concept of forward models, important advances have been made in understanding how
efference copies of motor commands are routed back to sensory structures for internally
monitoring movement (Sommer and Wurtz, 2008). Those signals, widely referred as corollary
discharges, have been observed across different species at many levels, including the cerebral
cortex, spinal cord, cerebellum and muscle spindles (Crapse and Sommer, 2008). Through such
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wide-spread feedback in the form of closed sensorimotor
loops, the brain is able to distinguish external motion from
self-generated movements (Angelaki and Cullen, 2008), update
sensory representations (Duhamel et al., 1992) and motor
execution (Azim et al., 2014), and optimize active sensation
(Kleinfeld and Deschênes, 2011). However, it is unclear where
and how re-afferent signals are integrated with sensory inputs
to form forward predictions leading to future movements, rather
than solely monitoring them.
Most studies in sensorimotor neurophysiology have utilized
reactive movements to stationary goals pre-defined by sensory
cues (Figure 1A left), but this approach is fundamentally
incapable of determining whether the observed neural
activity reflects sensory stimuli or predicts future states.
Exploring the neural codes of a forward model demands
the development of novel behavioral tasks that are highly
dependent on predictive spatiotemporal transformations, such
as interception (Figure 1A right), in which the movement
is directed to a predicted future location of a moving target,
as opposed to a static location explicitly specified by sensory
cues. Interception has been widely investigated in numerous
studies (see reviews Merchant and Georgopoulos, 2006;
Zago et al., 2009). In the temporal domain, Merchant et al.
(2004a,b) have shown that activity in both parietal and motor
cortices encode estimations of arrival time (Tau-coupling,
Lee, 1976) for precisely-timed interception at pre-determined
destinations. In contrast to a wealth of data on temporal
prediction, spatial extrapolation for interception has rarely been
addressed, and consequentially little is known about its neural
implementation.
During tracking and pursuing of moving stimuli,
neuronal activity faithfully conveying the instantaneous
or inferred target motion has been found in both cortical
(e.g., Assad and Maunsell, 1995; Ferrera and Barborica,
2010) and subcortical areas (e.g., Cui and Malpeli, 2003;
Ma et al., 2013). However, the neurons examined in these
studies were primarily involved in the formation of upto-date percepts, rather than in specifying the goals of the
intercepting movements. For example, during catch-up saccades
toward moving targets, pre-saccadic activity in the superior
colliculus (SC) encodes retinal position error, instead of actual
saccade metrics that take into account retinal slip (Keller
et al., 1996). It is unclear how the time involved in actual
generation and completion of the eye movements is taken into
account.
FIGURE 1 | Sensorimotor transformation and posterior parietal cortex
(PPC). (A) Unlike movement to a static target (left) in which motor parameters
are tightly linked to a fixed stimulus location, in flexible interception (right) the
brain not only compensates for sensory latency to estimate current stimulus
location St , but also predicts the future target location at reach offset St+MT to
direct the intercepting movement. (B) The PPC might play a proactive role in
predictive sensorimotor control by integrating visual (target location and
motion), somatosensory and efference copy signal to form forward models of
future object and body states.
posterior parietal cortex (PPC) is a plausible candidate for
mediating the fundamental relationship between sensory
prediction and motor control (Figure 1B). Examining
PPC activity during interceptive behaviors in dynamic
environments might provide deep insights into how the
brain constructs forward predictions for guiding movement,
because the internal prediction of future target location can
be inferred from the intercepting movements themselves.
If the PPC underlies the forward model, PPC neurons
should not merely convey the current states of body and
object, but also should predict the future consequences of
an impending movement. Indeed, accumulating evidence
indicates that PPC activity predicts the sensory consequences
of upcoming movements (Mulliken and Andersen, 2009).
Thus, the PPC might fulfill the goal of forward prediction
PLAUSIBLE ROLE OF THE PPC IN
FORWARD PREDICTION
Internal prediction of the sensory consequences of extra-personal
objects is, presumably, a high-order sensory representation
that might be embodied in association areas. Moreover,
forward prediction demands integrating different sources
of sensorimotor information through a rich pattern of
anatomical connectivity. As a crucial node incorporating
visual, proprioceptive and efference copy information
in a sensorimotor network (Andersen et al., 1997), the
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in sensorimotor transformations, and work in concert with
inverse models in the motor cortex and the subcortical
motor apparatus to implement them (Andersen and Cui,
2009).
Prediction is not only fundamental for motor control, but
may also be crucial for many aspects of cognition, including
sequential planning, decision making, social interaction, action
understanding, imitation and mental practice. Therefore,
forward prediction might offer a cohesive framework for
understanding the neural basis of many related behaviors.
For instance, neurological studies have shown that patients
with lesion in the left PPC suffer from ideational apraxia
(Buxbaum, 1998; Zadikoff and Lang, 2005). Although these
patients appear normal for simple movements, they are
impaired in associating objects with their purposes, and/or
in generating complicated action sequences. A plausible
interpretation is that the PPC damage interfered with the
prediction of outcomes and consequences of forthcoming
actions.
Growing evidence suggests that the PPC is composed
of multiple functional subareas with distinct roles in
the sensorimotor transformation (Andersen and Buneo,
2002; Cui, 2014). These include inferior parietal area 7a
and superior parietal area 5d, as well as their upstream
structures, and also the lateral intraparietal area (LIP) and
parietal reach region (PRR; Figure 1B). Area 7a is the
top structure in a dorsal visual hierarchy (Felleman and
Van Essen, 1991; Bastos et al., 2014), with rich inputs
from motion-sensitive extrastriate areas (Felleman and Van
Essen, 1991). Its activity is modulated by world-centered gain
fields (Snyder et al., 1998) and other top-down inputs from
the prefrontal cortex (Crowe et al., 2013), hippocampus
and cerebellum (Clower et al., 2001). Consequently, it
is ideally positioned to represent the visual prediction of
behaviorally relevant objects achieved through sensorimotor
learning.
As a counterpart of area 7a, area 5 initially was thought
of as a high-level somatosensory area (even sometimes
referred to as S3) that conveys more abstract information
about combined joint angles (Sakata et al., 1973). However,
subsequent studies on behaving monkeys have shown
that area 5 neurons convey kinematic information (Ashe
and Georgopoulos, 1994; Kalaska and Crammond, 1995)
for component arm movements (Li and Cui, 2013) in
body-related coordinates (Johnson et al., 1996; Bremner
and Andersen, 2012) by integrating visual and somatosensory
inputs according to the behavioral context (Brunamonti
et al., 2016). Given its close linkages to S1 and M1/PMd,
area 5 might predict the somatosensory consequence of
an upcoming arm movement during interception, with its
neural activity primarily associated with intrinsic kinematic
characteristics, such as limb trajectories, speed profiles and joint
angles.
To investigate neural activity when movements are directed
by anticipated sensory outcomes, rather than by current
perceived stimulus locations, we have recorded parietal activity
from monkeys performing a flexible manual interception
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FIGURE 2 | Feedforward control of manual interception in naturalistic
(A) and brain-controlled (B) conditions. (A) In a manual interception task,
the brain first predicts the future target location at movement offset, and then
directs the arm (end-effector) toward it through open-loop control of
musculoskeletal system. (B) In brain-controlled interception, predictive PPC
activity is decoded to an intended motor goal, and used as a control signal to
direct a cursor on a computer screen or a robotic actuator toward the future
location at interception, as opposed to actually reaching for it with the hand.
task involving dynamic sensory-motor contingencies. In
this paradigm, the monkey initiates a trial by positioning
a hand at the center of a touch screen. A peripheral target
moving at an angular velocity of 0 (control), 120, or
240◦ /s in a circular path appears in one of eight locations
spaced at 45◦ . The targets, which could be moving either
clockwise or counter clockwise, is visible for 1 s, have to
be intercepted by a hand movement within this interval.
During interception, a hand movement should be planned
toward the anticipated target location at interception to
accomplish the task. Therefore, if area 7a predicts the
visual consequences of the upcoming interception, its
pre-movement activity should encode movement destination,
rather than instantaneous stimulus location. Preliminary
data support this idea (Li et al., 2014). They suggest that
movement-directional tuning curves of pre-movement
activity are invariant, but stimulus-directional tuning curves
significantly shift as functions of target speed. Partial correlation
analysis demonstrates that PPC activity is more pronounced
for the reaching direction than for the stimulus location,
suggesting an intimate role in forward prediction and motor
planning.
DECODING PPC ACTIVITY FOR
FEEDFORWARD PROSTHETIC CONTROL
Over the past two decades, intracortically-based neuroprosthetics
have emerged as promising approaches to restoring sensorimotor
function for severely disabled patients suffering from nervous
diseases or injuries. Based on neural activity recorded
from chronically implanted multi-channel electrode arrays,
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intended movements have been successfully decoded as
command signals and used to manipulate a robotic device
or a cursor on a computer screen to replace the lost
motor function of a paralyzed limb (Serruya et al., 2002;
Carmena et al., 2003; Musallam et al., 2004; Hochberg
et al., 2006; Santhanam et al., 2006; Velliste et al., 2008;
Aflalo et al., 2015), even enabling it bimanually (Ifft et al.,
2013) or bi-directionally (Ethier et al., 2012). Although
such brain-machine interfaces (BMIs) have succeeded in
continuously driving prosthetic arms, the achieved performances
in movement speed, straightness and smoothness still fall
short of widespread clinical applicability. Unlike natural
movements planned in a feedforward manner, brain-controlled
prosthetic devices demand continuous guidance of decoded
neural signals, largely relying on visual feedback during
movement execution. Furthermore, in a dynamic world, it
is unlikely that closed-loop prosthetic systems depending on
sensory feedback control are feasible for capturing moving
objects.
Clinically viable BMI systems that enable real-time
interactions with dynamic environments demand translation
of predictive neural activity into desired motor goals to
guide ballistic movements, as in natural interception
(Figure 2A). Since our ongoing research has indicated
that pre-movement activity in the PPC is informative of
the intercepting movement, it is, in principle, possible to
decoded this activity and utilize it as a predictor of an
upcoming movement destination. Based on the decoded
endpoint position, a computer cursor or artificial limb
could be moved by a goal-directed open-loop controller
(Figure 2B). After the desired endpoint is extracted from the
PPC activity by an optimal estimation decoder (i.e., population
vector, machine learning algorithm), the artificial actuator
will be driven toward the intended movement goal while
receiving no further brain controls until landing. If the
actuator is a computer cursor, it will move to the goal in a
bell-shaped speed profile, as described by Fitts’ law. If the
actuator is a high-degree of freedom (DOF) robotic limb,
it will be driven by a goal-directed pre-coded program
fitted to its own mechanics, rather than by biomimetic
kinematics. Of course, how the central nervous system
compensates for prosthetic motor errors through learning and
adaptation to archieve proficient BMI control is a challenging
problem.
DISCUSSION
REFERENCES
movements. Annu. Rev. Neurosci. 20, 303–330. doi: 10.1146/annurev.neuro.20.
1.303
Angelaki, D. E., and Cullen, K. E. (2008). Vestibular system: the many facets of
a multimodal sense. Annu. Rev. Neurosci. 31, 125–150. doi: 10.1146/annurev.
neuro.31.060407.125555
Ashe, J., and Georgopoulos, A. P. (1994). Movement parameters and neural
activity in motor cortex and area 5. Cereb. Cortex 4, 590–600. doi: 10.
1093/cercor/4.6.590
Assad, J. A., and Maunsell, J. H. (1995). Neuronal correlates of inferred motion in
primate posterior parietal cortex. Nature 373, 518–521. doi: 10.1038/373518a0
Azim, E., Jiang, J., Alstermark, B., and Jessell, T. M. (2014). Skilled reaching relies
on a V2a propriospinal internal copy circuit. Nature 508, 357–363. doi: 10.
1038/nature13021
Even if predictive activity embodied in the PPC can provide
useful neural signals for open-loop prosthetic control of
movements, important issues remained to be addressed.
Predictive sensorimotor control engages a highly distributed
cortical network, so the PPC alone probably is inadequate
for directing comprehensive and adaptable motor repertoires.
Preferably, a PPC-based controller should be integrated with
neural signals from motor areas to implement timely and
accurate control of rapidly-moving actuators. Furthermore,
because successful interception requires not only accurate spatial
prediction, but also precise timing (Merchant and Georgopoulos,
2006; Zago et al., 2009), dynamic BMIs based on the PPC
should also incorporate temporal information conveyed in the
motor and premotor cortex (Merchant and Georgopoulos, 2006;
Merchant et al., 2009, 2011).
Despite the importance of forward models, movement
can still be modified by sensory feedback during execution
(Todorov and Jordan, 2002; Scott, 2004). According to the
behavioral context, natural interceptions might adopt reactive,
predictive, or mixed strategies (Port et al., 1997; Merchant
et al., 2003). Thus, prosthetic movements directed to an
endpoint decoded from PPC signals could be corrected
online by an artificial proprioceptive feedback via intracortical
microstimulation (Dadarlat et al., 2015), probably in the
deceleration phase.
In summary, BMIs capable of implementing predictive
sensorimotor control in dynamic circumstances require
systematic integration of modern motor neuroscience and
numerous sophisticated techniques. If successful, such an
approach may allow paralyzed patients to bypass their
immobilized bodies, and interact with the dynamic external
world.
AUTHOR CONTRIBUTIONS
HC conceived the study and wrote the article.
ACKNOWLEDGMENTS
The author thanks J.G. Malpeli for helpful edits on the
manuscript, and P. Ding, S. Guo, C. Li, M. Wang, T. Wang and
Y. Zhang for useful discussions.
Aflalo, T., Kellis, S., Klaes, C., Lee, B., Shi, Y., Pejsa, K., et al. (2015). Decoding
motor imagery from the posterior parietal cortex of a tetraplegic human.
Science 348, 906–910. doi: 10.1126/science.aaa5417
Andersen, R. A., and Buneo, C. A. (2002). Intentional maps in posterior parietal
cortex. Annu. Rev. Neurosci. 25, 189–220. doi: 10.1146/annurev.neuro.25.
112701.142922
Andersen, R. A., and Cui, H. (2009). Intention, action planning and decision
making in parietal-frontal circuits. Neuron 63, 568–583. doi: 10.1016/j.neuron.
2009.08.028
Andersen, R. A., Snyder, L. H., Bradley, D. C., and Xing, J. (1997). Multimodal
representation of space in the posterior parietal cortex and its use in planning
Frontiers in Integrative Neuroscience | www.frontiersin.org
4
October 2016 | Volume 10 | Article 35
Cui
PPC-Based Neuroprosthetics in Dynamic Environment
Bastos, A. M., Vezoli, J., Bosman, C. A., Schoffelen, J. M., Oostenveld, R., and
Dowdall, J. R. (2014). Visual areas exert feedforward and feedback influences
through distinct frequency channels. Neuron 85, 390–401. doi: 10.1016/j.
neuron.2014.12.018
Bremner, L. R., and Andersen, R. A. (2012). Coding of the reach vector in parietal
area 5d. Neuron 75, 342–351. doi: 10.1016/j.neuron.2012.03.041
Brunamonti, E., Genovesio, A., Pani, P., Caminiti, R., and Ferraina, S. (2016).
Reaching-related neurons in superior parietal area 5: influence of the target
visibility. J. Cogn. Neurosci. 28, 1828–1837. doi: 10.1162/jocn_a_01004
Buxbaum, L. J. (1998). Ideational apraxia and naturalistic action. Cogn.
Neuropsychol. 15, 617–643. doi: 10.1080/026432998381032
Carmena, J. M., Lebedev, M. A., Crist, R. E., O’Doherty, J. E., Santucci, D. M.,
Dimitrov, D. F., et al. (2003). Learning to control a brain-machine interface for
reaching and grasping by primates. PLoS Biol. 1:e42. doi: 10.1371/journal.pbio.
0000042
Clower, D. M., West, R. A., Lynch, J. C., and Strick, P. L. (2001). The inferior
parietal lobule is the target of output from the superior colliculus, hippocampus
and cerebellum. J. Neurosci. 21, 6283–6291.
Crapse, T. B., and Sommer, M. A. (2008). Corollary discharge across the animal
kingdom. Nat. Rev. Neurosci. 9, 587–600. doi: 10.1038/nrn2457
Crowe, D. A., Goodwin, S. J., Blackman, R. K., Sakellaridi, S., Sponheim, S. R.,
and MacDonald, A. W. III (2013). Prefrontal neurons transmit signals to
parietal neurons that reflect executive control of cognition. Nat. Neurosci. 16,
1484–1491. doi: 10.1038/nn.3509
Cui, H. (2014). From intention to action: hierarchical sensorimotor
transformation in the posterior parietal cortex. eNeuro 1:e0017-14.2014.
doi: 10.1523/ENEURO.0017-14.2014
Cui, H., and Malpeli, J. G. (2003). Activity in the parabigeminal nucleus during
eye movements directed at moving and stationary targets. J. Neurophysiol. 89,
3128–3142. doi: 10.1152/jn.01067.2002
Dadarlat, M. C., O’Doherty, J. E., and Sabes, P. N. (2015). A learning-based
approach to artificial sensory feedback leads to optimal integration. Nat.
Neurosci. 18, 138–144. doi: 10.1038/nn.3883
Duhamel, J. R., Colby, C. L., and Goldberg, M. E. (1992). The updating of the
representation of visual space in parietal cortex by intended eye movements.
Science 55, 90–92. doi: 10.1126/science.1553535
Ethier, C., Oby, E. R., Bauman, M. J., and Miller, L. E. (2012). Restoration of grasp
following paralysis through brain-controlled stimulation of muscles. Nature
485, 368–371. doi: 10.1038/nature10987
Felleman, D. J., and Van Essen, D. C. (1991). Distributed hierarchical processing
in the primate cerebral cortex. Cereb. Cortex 1, 1–47. doi: 10.1093/cercor/1.1.1
Ferrera, V. P., and Barborica, A. (2010). Internally generated error signals in
monkey frontal eye field during an inferred motion task. J. Neurosci. 30,
11612–11623. doi: 10.1523/JNEUROSCI.2977-10.2010
Franklin, D. W., and Wolpert, D. M. (2011). Computational mechanisms of
sensorimotor control. Neuron 72, 425–442. doi: 10.1016/j.neuron.2011.10.006
Hochberg, L. R., Serruya, M. D., Friehs, G. M., Mukand, J. A., Saleh, M., Caplan,
A. H., et al. (2006). Neuronal ensemble control of prosthetic devices by a human
with tetraplegia. Nature 442, 164–171. doi: 10.1038/nature04970
Ifft, P. J., Shokur, S., Li, Z., Lebedev, M. A., and Nicolelis, M. A. L. (2013). A brainmachine interface enables bimanual arm movements in monkeys. Sci. Transl.
Med. 5:210ra154. doi: 10.1126/scitranslmed.3006159
Johnson, P. B., Ferraina, S., Bianchi, L., and Caminiti, R. (1996). Cortical networks
for visual reaching: physiological and anatomical organization of frontal and
parietal lobe arm regions. Cereb. Cortex 6, 102–119. doi: 10.1093/cercor/6.2.
102
Kalaska, J. F., and Crammond, D. J. (1995). Deciding not to GO: neuronal
correlates of response selection in a GO/NOGO task in primate premotor and
parietal cortex. Cereb. Cortex 5, 410–428. doi: 10.1093/cercor/5.5.410
Keller, E. L., Gandhi, N. J., and Weir, P. T. (1996). Discharge of superior
collicular neurons during saccades made to moving targets. J. Neurophysiol. 76,
3573–3577.
Kleinfeld, D., and Deschênes, M. (2011). Neuronal basis for object location in
the vibrissa scanning sensorimotor system. Neuron 72, 455–468. doi: 10.1016/j.
neuron.2011.10.009
Lee, D. N. (1976). A theory of visual control of braking based on
information about time-to-collision. Perception 5, 437–459. doi: 10.1068/p0
50437
Frontiers in Integrative Neuroscience | www.frontiersin.org
Li, Y., and Cui, H. (2013). Dorsal parietal area 5 encodes immediate reach
in sequential arm movements. J. Neurosci. 33, 14455–14465. doi: 10.
1523/JNEUROSCI.1162-13.2013
Li, Y., Wang, Y., and Cui, H. (2014). Posterior parietal cortex predicts
upcoming movement during manual interception of moving targets. Society
for Neuroscience Abstract, 437.03.
Ma, R., Cui, H., Lee, S. H., Anastasio, T. J., and Malpeli, J. G. (2013).
Predictive encoding of moving target trajectory by neurons in the
parabigeminal nucleus. J. Neurophysiol. 109, 2029–2043. doi: 10.1152/jn.
01032.2012
Merchant, H., Battaglia-Mayer, A., and Georgopoulos, A. P. (2003).
Interception of real and apparent motion targets: psychophysics in humans
and monkeys. Exp. Brain Res. 152, 106–112. doi: 10.1007/s00221-0031514-5
Merchant, H., Battaglia-Mayer, A., and Georgopoulos, A. P. (2004a). Neural
responses during interception of real and apparent circularly moving
stimuli in motor cortex and area 7a. Cereb. Cortex 14, 314–331. doi: 10.
1093/cercor/bhg130
Merchant, H., Battaglia-Mayer, A., and Georgopoulos, A. P. (2004b). Neural
responses in motor cortex and area 7a to real and apparent motion. Exp. Brain
Res. 154, 291–307. doi: 10.1007/s00221-003-1664-5
Merchant, H., and Georgopoulos, A. P. (2006). Neurophysiology of perceptual and
motor aspects of interception. J. Neurophysiol. 95, 1–13. doi: 10.1152/jn.00422.
2005
Merchant, H., Zarco, W., Pérez, O., Prado, L., and Bartolo, R. (2011).
Measuring time with different neural chronometers during a
synchronization-continuation task. Proc. Natl. Acad. Sci. U S A 108,
19784–19789. doi: 10.1073/pnas.1112933108
Merchant, H., Zarco, W., Prado, L., and Pérez, O. (2009). Behavioral and
neurophysiological aspects of target. Adv. Exp. Med. Biol. 629, 201–220. doi: 10.
1007/978-0-387-77064-2_10
Mulliken, G. H., and Andersen, R. A. (2009). ‘‘Forward models and
state estimation in posterior parietal cortex,’’ in The Cognitive
Neurosciences IV, ed. M. S. Gazzaniga (Cambridge, MA: MIT Press),
599–611.
Musallam, S., Corneil, B. D., Greger, B., Scherberger, H., and Andersen, R. A.
(2004). Cognitive control signals for neural prosthetics. Science 305, 258–262.
doi: 10.1126/science.1097938
Nijhawan, R. (1994). Motion extrapolation in catching. Nature 370, 256–257.
doi: 10.1038/370256b0
Nijhawan, R. (2008). Visual prediction: psychophysics and neurophysiology of
compensation for time delays. Behav. Brain Sci. 31, 179–198; discussion
198–239. doi: 10.1017/S0140525X08003804
Nijhawan, R., and Wu, S. (2009). Compensating time delays with neural
predictions: are predictions sensory or motor? Philos. Trans. A Math. Phys. Eng.
Sci. 367, 1063–1078. doi: 10.1098/rsta.2008.0270
Port, N., Lee, D., Dassonville, P., and Georgopoulos, A. P. (1997). Manual
interception of moving targets I. Performance and movement initiation. Exp.
Brain Res. 116, 406–420. doi: 10.1007/pl00005769
Sakata, H., Takaoka, Y., Kawarasaki, A., and Shibutani, H. (1973). Somatosensory
properties of neurons in the superior parietal cortex (area 5) of the
rhesus monkey. Brain Res. 64, 85–102. doi: 10.1016/0006-8993(73)
90172-8
Santhanam, G., Ryu, S. I., Yu, B. M., Afshar, A., and Shenoy, K. V. (2006).
A high-performance brain-computer interface. Nature 442, 195–198. doi: 10.
1038/nature04968
Scott, S. H. (2004). Optimal feedback control and the neural basis of
volitional motor control. Nat. Rev. Neurosci. 5, 532–546. doi: 10.1038/
nrn1427
Serruya, M., Hatsopoulos, N. G., Paninski, L., Fellows, M. R., and Donoghue, J. P.
(2002). Brain-machine interface: instant neural control of a movement signal.
Nature 416, 141–142. doi: 10.1038/416141a
Shadmehr, R., and Mussa-Ivaldi, S. (2012). Biological Learning and Control:
How the Brain Builds Representations, Predicts Events and Makes Decisions.
Cambridge, MA: MIT Press.
Snyder, L. H., Grieve, K. L., Brotchie, P., and Andersen, R. A. (1998). Separate
body-and world-referenced representations of visual space in parietal cortex.
Nature 394, 887–891. doi: 10.1038/29777
5
October 2016 | Volume 10 | Article 35
Cui
PPC-Based Neuroprosthetics in Dynamic Environment
Sommer, M. A., and Wurtz, R. H. (2008). Brain circuits for the internal monitoring
of movements. Annu. Rev. Neurosci. 31, 317–338. doi: 10.1146/annurev.neuro.
31.060407.125627
Todorov, E., and Jordan, M. I. (2002). Optimal feedback control as a
theory of motor coordination. Nat. Neurosci. 5, 1226–1235. doi: 10.1038/
nn963
Velliste, M., Perel, S., Spalding, M. C., Whitford, A. S., and Schwartz, A. B. (2008).
Cortical control of a prosthetic arm for self-feeding. Nature 453, 1098–1101.
doi: 10.1038/nature06996
Wolpert, D. M., Ghahramani, Z., and Jordan, M. I. (1995). Are arm trajectories
planned in kinematic or dynamic coordinates? An adaptation study. Exp. Brain
Res. 103, 460–470. doi: 10.1007/bf00241505
Zadikoff, C., and Lang, A. E. (2005). Apraxia in movement disorders. Brain 128,
1480–1497. doi: 10.1093/brain/awh560
Frontiers in Integrative Neuroscience | www.frontiersin.org
Zago, M., McIntyre, J., Senot, P., and Lacquaniti, F. (2009). Visuo-motor
coordination and internal models for object interception. Exp. Brain Res. 192,
571–604. doi: 10.1007/s00221-008-1691-3
Conflict of Interest Statement: The author declares that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
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October 2016 | Volume 10 | Article 35